GEO DATA

Most-download articles are from the articles published in 2023 during the last three month.

Original Papers

Exploring Wild Bees Diversity in Seocheon Maeul-Soop: A Quantitative Study: Sanghun Lee, Ohchang Kwon, Dong Su Yu, Jeong-Seop An, Na-Hyun Ahn; GEO DATA. 2024;6(1):1-7. Published online March 26, 2024; DOI: https://doi.org/10.22761/GD.2024.0003

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Abstract PDF: Wild bees are important pollinators in the ecosystem, and it is important to monitor their abundance and diversity to characterize and conserve these pollinators. In this study, wild bees were collected from a Maeul-soop in Seocheon-gun, Chungcheongnam-do, Republic of Korea for 2 years from February 2019 to October 2020. From the survey, a total of 3,258 wild bees from 9 families and 57 species were collected over 2 years in the Maeul-soop. The most dominant species was the Andrena kaguya, followed by the Apis mellifera, the Eucera spurcatipes, the Seladonia aeraria, and the Lasioglossum sibiriacum. Monthly changes in the number of species and populations show that the number of species increased from February and peaked in August, and the population peaked in April and then decreased. In addition, in the list of wild bee species collected over the past 2 years, the Apidae was the largest with 16 species, followed by the Halictidae with 13 species and the Megachilidae with nine species. However, although there is only one species of Andrena kaguya in the Andrenidae, its population is 2,084, which is the largest among all wild bees investigated in this study. The results of this study will be useful in understanding the impact of pollinating insects due to climate change in the future.

Quantitative Study of Butterfly Diversity in Wando Quercus acuta Forest Over 5 Years (2017-2021): Sanghun Lee, Na-Hyun Ahn; GEO DATA. 2023;5(2):55-59. Published online June 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0010

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Abstract PDF: This study presents the long-term quantitative data on butterflies in Wando Arboretum, which represents the only warm-temperate forest located in the southernmost part of South Korea. This arboretum has significant academic value as approximately 770 species of rare woody plants or herbs, such as the Japanese evergreen oak (Quercus acuta), found in warm temperate zones grow under natural conditions here. In this project, the butterflies in this region were studied due to their sensitivity to temperature changes. The study was conducted from March-April to October-November over 5 years (2017-2021) in the region dominated by Japanese evergreen oak. We found 1,743 individuals of 47 butterfly species belonging to five families. The acquired butterfly data could serve as a reference for the further development of a network-oriented database for assessing temporal climate changes.

Data Articles

GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2025;7(1):36-44. Published online February 13, 2025; DOI: https://doi.org/10.22761/GD.2024.0054

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Abstract PDF: Air pollution in East Asia presents critical environmental and health challenges, particularly in industrial regions affected by domestic and cross-border emissions. This study developed a GEO AI dataset specifically for industrial park segmentation, integrating Sentinel-2 satellite imagery, Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite data, and Air Quality Monitoring Network data. Optimized for semantic segmentation tasks with labeled data specifically for industrial park classification, this dataset serves as a foundational asset for the precise identification and spatial tracking of major air pollution sources. We validated the dataset’s applicability using a modified U-Net model, achieving a mean intersection over union of 0.8146 and pixel accuracy of 0.9608, thereby demonstrating its potential as a tool for detecting and monitoring pollutant sources in industrial areas. With future expansion through additional temporal data and diverse pollutant measurements, this dataset is anticipated to support regional air quality monitoring efforts and inform strategies for pollution control across East Asia.

KOMPSAT-3/3A Image-text Dataset for Training Large Multimodal Models: Han Oh, Dong-Bin Shin, Dae-Won Chung; GEO DATA. 2025;7(1):27-35. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2025.0003

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Abstract PDF: This study aims to improve the accuracy and interpretability of large multimodal models (LMMs) specialized in satellite image analysis by constructing an image-text dataset based on KOMPSAT-3/3A imagery and presenting the results of training using this dataset. Conventional LMMs are primarily trained on general images, limiting their ability to effectively interpret the specific characteristics of satellite imagery, such as spectral bands, spatial resolution, and viewing angles. To address this limitation, we developed an image-text dataset, divided into pretraining and finetuning stages, based on the existing KOMPSAT object detection dataset. The pretraining dataset consists of captions summarizing the overall theme and key information of each image. The fine-tuning dataset integrates metadata -including acquisition time, sensor type, and coordinates- with detailed object detection labels to generate six types of question-answer pairs: detailed descriptions, conversations with varying answer lengths, bounding box identification, multiple choice questions, and complex reasoning. This structured dataset enables the model to learn not only the general context of satellite images but also fine-grained details such as object quantity, location, and geographic attributes. Training with the new KOMPSAT-based dataset significantly improved the model’s accuracy in recognizing regional information and object characteristics in satellite imagery. Finetuned models achieved substantially higher accuracy than previous models, surpassing even the GPT-4o model and demonstrating the effectiveness of a domain-specific dataset. The findings of this study are expected to contribute to various remote sensing applications, including automated satellite image analysis, change detection, and object detection.

Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data: DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim; GEO DATA. 2025;7(1):18-26. Published online February 5, 2025; DOI: https://doi.org/10.22761/GD.2024.0052

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Abstract PDF: In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R² core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.

A Study on the Spatial Information Compilation of Inland Wetlands in South Korea: Chang-Su Lee, Haeseon Shin, Hyeongcheol Lee, Yijung Kim, Sanghun Lee; GEO DATA. 2024;6(4):226-234. Published online December 4, 2024; DOI: https://doi.org/10.22761/GD.2024.0034

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Abstract PDF: Wetlands offer numerous benefits, including improving water quality, providing habitats for wildlife, and storing water. They are areas where water either covers the soil or is just below the surface for extended periods. Wetlands play a crucial role in maintaining environmental balance and ecological stability. In South Korea, the Wetlands Conservation Act was established in 1999 to protect these vital ecosystems and their biodiversity. The law defines inland wetlands as lakes, ponds, swamps, rivers, and estuaries. However, the boundaries of these areas are often unclear, creating challenges for conservation and research. This ambiguity complicates effective management and the implementation of necessary protective measures. This study utilized topographic and aerial images to gather spatial information about inland wetlands and assess their areas. It identified the boundaries of inland wetlands in South Korea, revealing a total area of 3,833.452 km², which is 3.8% of the country’s total land area. The classified the spatial data, showing that vegetated areas cover 1,355.666 km², or 35.4% of the total area, with woody plants covering 102.987 km² and herbaceous plants 1,252.679 km². Non-vegetated areas account for 2,477.786 km², or 64.6%, with open water 2,206.615 km², natural land 160.995 km², artificial land 72.343 km², and Agricultural land 37.833 km². Clearly defining wetland boundaries is essential for effective conservation and protection. Accurate boundary definitions facilitate legal protection and help prevent damage to wetlands. The results provide quantitative data that can inform future wetland conservation planning and management. And enhance our understanding of the size and changes in South Korea’s inland wetlands, supporting their preservation and protection.

Original Paper

Construction of Exploration Data for Greenhouse Gas Geologic Storage: Focusing on Geological Cross-section Data: Bokyun Ko, Sungjae Park, Saro Lee; GEO DATA. 2023;5(3):222-229. Published online September 26, 2023; DOI: https://doi.org/10.22761/GD.2023.0023

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Abstract PDF: In this study, the most basic data, underground geological structure data, that is, geological cross-section data, were established to select a candidate site for underground storage of greenhouse gases based on AI. As a target area, the Gyeongsang Basin, where a large amount of sedimentary rocks are distributed, was selected as the greenhouse gas can be stored most effectively in sedimentary rocks. To this end, the acquisition and edit step, the refinement step, and the labeling step were carried out in the order of raw data collection, source data and labeling data construction to construct the geological cross-section data. This data can be downloaded through the AI hub site (https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71390) operated by the Korea Institute for Intelligent Information Society Promotion.

Data Articles

Characteristics of Plant Communities Distribution in the Estuarine Wetlands along the East and South Coasts of Korea: Yeounsu Chu, Pyoungbeom Kim, Sanghun Lee; GEO DATA. 2025;7(1):9-17. Published online March 24, 2025; DOI: https://doi.org/10.22761/GD.2025.0005

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Abstract PDF: This study was conducted to investigate the distribution patterns and ecological characteristics of plant communities in estuarine wetland in South Korea. A total of 288 estuarine wetlands were surveyed, revealing that vegetated areas accounted for 38.3 km², while non-vegetated areas, predominantly water bodies (133.2 km²), covered 143.8 km². The high proportion of water areas in estuarine wetlands (approximately 70%) contrasts with the 50.8% recorded in inland wetlands, reflecting the challenging conditions for plant establishment due to the continuous mixing of fresh and saline waters. A total of 167 plant communities were identified, with reed (Phragmites australis) communities occupying the largest area (26.0 km²). The analysis of habitat preferences revealed that the majority of the plant communities were categorized as obligate wetland plants (47 species) and facultative wetland plants (12 species), with halophytes playing a significant role in maintaining biodiversity in these ecosystems. Comparative analysis between the East and South coasts showed significant differences in the distribution of wetland and halophytic plant communities, suggesting that the distinct geomorphological and ecological conditions of each region strongly influence plant community structures. These research results will provide a scientific basis for the conservation and management of estuarine wetland ecosystems.

Changes in Fish Fauna and Community Analysis in Upo Wetland Protection Area, South Korea: Su Hwan Kim, Young-Jin Yun, Dae-Yeol Bae, Sangwook Han, Yeounsu Chu; GEO DATA. 2024;6(4):271-279. Published online December 16, 2024; DOI: https://doi.org/10.22761/GD.2024.0030

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Abstract PDF: Upo Wetland is a representative wetland protection area in Korea, recognized for its high conservation value as a habitat for various organisms. However, the presence of livestock farms and farmland around Upo Wetland has led to the inflow of various pollutants, resulting in mass fish deaths. To protect and manage the freshwater fish residing in Upo Wetland, we analyzed the changes in fish fauna and community structure using survey data collected from 2003 to 2024. The survey confirmed the presence of 13,149 individuals belonging to 12 families and 30 species of fish, with Pseudorasbora parva being the dominant species, followed by Carassius auratus. Micropterus salmoides and Lepomis macrochirus, invasive alien species, were prevalent in 2003 and 2009 but showed a rapid deline after 2021, while Erythroculter erythropterus increased significantly. Another alien species, Oreochromis niloticus, appeared intermittently, and the Rhodeus uyekii and Aphyocypris chinensis have not been observed since 2006, leading to the presumption that they have disappeared from Upo Wetland. Rhinogobius brunneus, Rhinogobius giurinus, and Tridentiger brevispinis have only been recorded since 2021, suggesting they were introduced after the 2020s.

Characteristics of Water Quality and Sediment Distributions on the Northeastern Coast of Jeju Island: Taehee Lee, Hyung Jeek Kim; GEO DATA. 2025;7(1):45-54. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2024.0050

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Abstract PDF: Since the 1980s, the number of land-based fish farms on Jeju Island has increased rapidly. With increasing land-based fish farms, a large amount of nutrients from fish farm wastewater is discharged off the coast of Jeju. To understand the characteristics of coastal seawater and the ecological environment on the coast of Jeju, the effect of land-based fish farm effluent on coastal seawater should be evaluated. Temperature, salinity, nutrients, and chlorophyll-a concentration were investigated on the northeastern coast of Jeju during June and July 2023. Nitrate, phosphate, and silicate concentrations in the surface waters were significantly higher in coastal stations than in the outer stations. Unlike the surface waters, nutrient concentrations in the bottom waters are distinctly higher in land-based fish farm effluent stations than in the outer stations. Total organic carbon content in surface sediment was significantly higher in land-based fish farm effluent stations than in the outer stations. This study may provide valuable information for evaluating the impact of land-based fish farm effluent on coastal ecosystems on Jeju Island.

Expanded Bioclimatic Variables Extracted from Monthly Climate Predictions under the SSP Climate Scenarios over South Korea: Jieun Oh, Ah Reum Han, Yeong-cheol Kim, Seungbum Hong; GEO DATA. 2024;6(4):235-247. Published online December 3, 2024; DOI: https://doi.org/10.22761/GD.2024.0018

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Abstract PDF: Numerous studies, including the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report, have documented species habitat shifts caused by climate change. These shifts lead to transformations in ecosystem structure, components, and functions. Exploring the connections between species and climate change is essential for developing adaptation strategies. Many studies use species distribution models (SDMs), which are based on the correlation between species habitats and climatic surroundings, to predict ecological shifts under climate change. The primary climate variables for these models are the only 19 variables whose concepts are based on monthly average temperature and precipitation from the BIOCLIM package developed in 1984. These 19 bioclimatic variables usually are obtained from WorldClim data set and other datasets. However, they have limitations in reflecting local climate characteristics and their association with ecology. Firstly, future projection data from global dataset including WorldClim dataset is derived directly from global climate models rather than regional climate models. Secondly, the 19 variables based on monthly temperature and precipitation do not adequately express hydrological characteristics of terrestrial ecosystem which are crucial for species habitats. Lastly, although there are various biogeographical indices excepts the 19 bioclimatic variables, there have been just a few cases that they were applied to SDMs for Korea. To overcome these limitations, this study expands the various bioclimatic variables, using regionally specialized climate data from Korea Meteorology Administration (KMA). The newly extended indices, which can reflect water availability, are expected to improve the prediction of SDMs, enabling more precise assessment of ecological risks due to climate change and effective adaptation strategies to mitigate the impacts of climate change on ecosystems.

Review Paper

Global Geospatial Data for Flood and Landslide Susceptibility Mapping: Saro Lee, Rezaie Fatemeh; GEO DATA. 2023;5(4):380-393. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0058

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Abstract PDF: Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.

Data Articles

Geological Age Data of the Ross Orogeny in the Terra Nova Intrusive Complex, Northern Victoria Land, Antarctica: Sang-Bong Yi, Mi Jung Lee; GEO DATA. 2025;7(1):1-8. Published online February 27, 2025; DOI: https://doi.org/10.22761/GD.2025.0001

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Abstract PDF: This study aims to establish the timing of the formation of the Terra Nova Intrusive Complex (TNIC), located in Terra Nova Bay, northern Victoria Land, Antarctica. The TNIC is paleogeographically situated inland of the Wilson Terrane of northern Victoria Land and was formed during the Paleozoic Ross Orogeny. This study obtained and compiled sensitive high-resolution ion microprobe zircon U-Pb ages for five intrusive bodies in the south-central part of the TNIC. The results clarify the formation age (about 530-470 Ma) of the TNIC and the interrelationships among the various intrusive units, especially those in the south-central region. The Confusion Intrusive Unit was emplaced at 520-515 Ma. The Russell Gabbro intruded into the Confusion Intrusive Unit at 501±3 Ma. Approximately at the same time (about 500 Ma), the Vegetation Intrusive Unit formed, followed by the Abbott Intrusive Unit at about 485 Ma. These findings provide valuable data for interpreting the geological development of the Antarctic Continent and the evolutionary history of the Ross Orogeny.

Study on Grain Size, Physical Properties and Organic Matter Characteristics of Tidal Flat Surface Sediments: May 2022 Hwangdo Tidal Flat Dataset, Cheonsu Bay: Jun-Ho Lee, Hoi-Soo Jung, Huigyeong Ryu, Keunyong Kim, Joo-Hyung Ryu, Yeongjae Jang; GEO DATA. 2024;6(3):159-174. Published online September 30, 2024; DOI: https://doi.org/10.22761/GD.2024.0011

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Abstract PDF: This study analyzes the geological and geochemical features of surface sediments in the Hwangdo Tidal Flat, located on Korea’s West Coast. The tidal flat experiences semi-diurnal tides, impacting organic matter decomposition and nutrient cycling. Ninety one sediment samples were collected and analyzed for physical and chemical properties including grain size, density, water content, organic carbon, and nitrogen. Sediments consist mainly of sand and silt, with coarser sediments near the main channel and finer sediments towards the west. Sediment grain size averages 4.12 Φ with a sorting coefficient of 1.96 Φ, indicating diverse energy environments. Total organic carbon and nitrogen correlate positively with grain size and density, reflecting sediment origin and environment. Kriging maps sediment grain size distribution, while correlation and linear regression analyses show relationships between variables. High correlations exist between various parameters, aligning with tidal flat characteristics and aiding understanding of sediment transport and deposition. The study provides baseline data for understanding the tidal flat’s geological, geochemical, and physical aspects, valuable for remote sensing validation and environmental monitoring. The dataset is freely available for research and management purposes.

Climate Change Impacts on the Potential Distribution of Camellia japonica in South Korea under SSP Scenarios: Jaeho Lee, Sungsoo Yoon, Jaeseok Lee, Changwan Seo; GEO DATA. 2024;6(4):400-410. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0058

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Abstract PDF: Climate change presents significant challenges to the habitat suitability of Camellia japonica, a key species in East Asian ecosystems. This study evaluated the potential impacts of climate change on the distribution of Camellia japonica in South Korea using species distribution models (SDMs) and shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Eleven SDMs, including an ensemble model weighted mean (EMW), were applied to predict habitat suitability from 2010 to 2070. Model performance was assessed using receiver operating characteristics curve (ROC), true skill statistics (TSS), and Kappa statistics, with EMW achieving the highest accuracy (ROC mean, 0.941), validating its reliability for integrated predictions. Bioclimatic variables, specifically mean diurnal range (BIO2) and annual precipitation (BIO12), were identified as the most influential factors driving habitat suitability. Under low-emission scenarios (SSP1-2.6 and SSP2-4.5), Camellia japonica habitats remained stable or showed slight expansion, maintaining suitability across coastline and central inland regions. Under high-emission scenarios (SSP3-7.0 and SSP5-8.5), Its habitat is expected to expand more rapidly and extensively across the eastern coastline and inland regions. Habitat shifts towards higher altitudes were observed across all scenarios, suggesting potential refugia in mountainous regions. This study highlights the vulnerability of Camellia japonica to climate change and the critical need for emission reduction policies to mitigate habitat loss. The findings provide actionable insights for conservation planning, emphasizing adaptive management strategies, such as preserving high-altitude refuges and restoring lowland habitats. Future research should incorporate additional ecological factors and higher-resolution datasets to refine predictions and guide sustainable biodiversity conservation efforts.

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